The present invention relates to processing of image data and in particular to enhancing quality and compressibility of digital images including combinations of text, graphics, and natural images by selective smoothing/denoising and selective sharpening.
Digital image data is often processed to enhance the visual quality of the image. Common image processing techniques include image smoothing and image sharpening. Smoothing is a technique that is mainly performed for reducing certain types of noise. Non-selective (or linear) smoothing algorithms smooth all features in an image (i.e., areas in the image which can be characterized as flat regions and areas within the image which can be characterized as edges). However, it is undesirable to smooth edges since smoothed edges gives the image a xe2x80x9cblurryxe2x80x9d appearance. Moreover, although smoothing is effective in removing most Gaussian noise, it is less effective in removing high amplitude noise such as speckle noise. Speckle noise can be characterized as a single unintentional black dot in a white region or a single unintentional white dot in a black region.
Generally, sharpening is a technique in which the edges within the image are sharpened to improve the visual quality of an image. This technique is often performed to enhance the visual quality of text or graphics within an image. One disadvantage of non-selective sharpening techniques is that they also tend to amplify noise.
Selective (or non-linear) filters such as selective smoothing or selective sharpening filters overcome the disadvantages of non-selective filters by applying the filtering function only to the features that are to be smoothed/sharpened while preserving the non-selected features. Selective filters include some means of identifying or differentiating between features, so that the filter is applied only to the desired feature. One example of an edge preserving, selective smoothing filter is an anisotropic diffusion filter.
Due to its denoising nature, the anisotropic diffusion filtering technique has recently been considered for enhancing compressibility. Specifically, an anisotropic diffusion filter was iteratively applied to image data between 10 and 20 times to obtain an optimal ratio between a visual quality measure and the bit-per-pixel (bpp) compression rate. It was found that applying 5 iterations produced images that were perceptually equivalent to the original images and the compression bit-rate was improved by 5%-17.5%. Although this technique shows that anisotropic diffusion can be used to improve image compressibility, it is impractical for real-time image processing applications such as image scanning since many time consuming iterations are required to obtain the desired image quality and compressibility. In addition, the conventional anisotropic diffusion technique does not clean speckle noise and other types of high amplitude noise and is thus insufficient for pre-processing scanned document images.
Another denoising/smoothing filtering technique that has been suggested for compression enhancement applications is a Sigma-filter that is even more computationally expensive per iteration than anisotropic diffusion filtering (although it requires less iterations for achieving the same noise reduction). However, this technique is still not fast enough for applications for processing full-page images or real-time image processing. Like the anisotropic diffusion filter, the Sigma-filter also does not remove high amplitude noise.
Finally, in the case of both denoising techniques (i.e., anisotropic diffusion filtering and Sigma pre-processing) each was considered for processing only natural images. However, application of these techniques on document images containing text, graphics, and natural images was not considered since it is well known that denoising filters (particularly applied in many iterations) degrade the quality of textual and graphical images. Specifically, edge sharpness of text features is degraded.
What is needed is an image processing technique which can be applied to combination-type (i.e., text, graphical, natural) document images and which increases compressibility while enhancing image quality with low computational complexity for real-time applications.
A system and method of enhancing image data and increasing compressibility of data by selectively smoothing the image data while preserving edges and selectively sharpening image data using variable contrast stretching. In one embodiment, variable contrast stretching is performed by clipping those pixel intensity values outside of a variable range and mapping those pixel intensity values within the variable range. In another embodiment, selective smoothing is performed using a robust anistotropic diffusion (RAD) filter.